{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:31:46Z","timestamp":1784737906474,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Science and Technology Project of Henan Province University","award":["26B413002"],"award-info":[{"award-number":["26B413002"]}]},{"name":"Henan Provincial Key Research and Development Program","award":["251111314000"],"award-info":[{"award-number":["251111314000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tongue coating is a crucial diagnostic indicator in traditional Chinese medicine, intuitively reflecting the body\u2019s physiological and pathological conditions. However, traditional visual inspection methods are highly susceptible to subjective bias, often resulting in diagnostic deviations and inconsistencies. To address these limitations, this study proposes an intelligent tongue coating diagnostic model based on an enhanced YOLOv13. The model integrates a hybrid architecture of swin transformer and YOLOv13, effectively capturing global contextual and local textural features for fine-grained recognition and analysis of tongue coating characteristics. Experimental results show that the enhanced model substantially outperforms the original YOLOv13 in fine-grained feature extraction and boundary localization, establishing a reliable foundation for the objectification, standardization, and intelligent advancement of tongue diagnosis in traditional Chinese medicine.<\/jats:p>","DOI":"10.3390\/s26010219","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:33:25Z","timestamp":1767191605000},"page":"219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["YOLOv13-SwinTongue: Tongue Coating Diagnosis Using an Enhanced YOLOv13 with Swin Transformer"],"prefix":"10.3390","volume":"26","author":[{"given":"Xiangqiang","family":"Yang","sequence":"first","affiliation":[{"name":"Henan Communications Engineering Co., Ltd., Zhengzhou 450044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinchao","family":"Hao","sequence":"additional","affiliation":[{"name":"Henan Communications Engineering Co., Ltd., Zhengzhou 450044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonggang","family":"Wang","sequence":"additional","affiliation":[{"name":"Henan Communications Engineering Co., Ltd., Zhengzhou 450044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfeng","family":"Man","sequence":"additional","affiliation":[{"name":"Henan Communications Engineering Co., Ltd., Zhengzhou 450044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5101-1576","authenticated-orcid":false,"given":"Renjie","family":"Yang","sequence":"additional","affiliation":[{"name":"Environmental Science, The University of Queensland, Brisbane, QLD 4067, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4114-497X","authenticated-orcid":false,"given":"Qinge","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jia, Y., Li, F., Zhou, W., and Xu, W. (2024, January 26\u201328). Applying image processing and analysis methods in TCM tongue inspection diagnosis: A brief survey. Proceedings of the 2024 4th International Conference on Bioinformatics and Intelligent Computing, Beijing, China.","DOI":"10.1145\/3665689.3665696"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.dcmed.2024.09.004","article-title":"Deep learning-based recognition of stained tongue coating images","volume":"7","author":"Zhong","year":"2024","journal-title":"Digit. Chin. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"207","DOI":"10.3233\/THC-248018","article-title":"Intelligent quality control of traditional chinese medical tongue Diagnosis Images Based on Deep Learning","volume":"32","author":"Zhou","year":"2024","journal-title":"Technol. Health Care"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Co\u015fgun Baybars, S., Talu, M.H., Danac\u0131, \u00c7., and Tuncer, S.A. (2025). Artificial Intelligence in Oral Diagnosis: Detecting Coated Tongue with Convolutional Neural Networks. Diagnostics, 15.","DOI":"10.3390\/diagnostics15081024"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhao, B., Zhang, Q., Li, R., Kong, X., and Wang, P. (2023, January 27\u201329). Swin-resnet: Research and implementation of a tooth-marked tongue classification method combining resnet-50 and swin transformer. Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science, Chengdu, China.","DOI":"10.1145\/3644116.3644271"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"66361","DOI":"10.1109\/ACCESS.2021.3076604","article-title":"Research on multiple-instance learning for tongue coating classification","volume":"9","author":"Tang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tiryaki, B., Torenek-Agirman, K., Miloglu, O., Korkmaz, B., Ozbek, \u0130.Y., and Oral, E.A. (2024). Artificial intelligence in tongue diagnosis: Classification of tongue lesions and normal tongue images using deep convolutional neural network. BMC Med. Imaging, 24.","DOI":"10.1186\/s12880-024-01234-3"},{"key":"ref_8","first-page":"7961494","article-title":"Diagnostic method of diabetes based on support vector machine and tongue images","volume":"2017","author":"Zhang","year":"2017","journal-title":"BioMed Res. Int."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kundu, P.K., and Kundu, M. (2016, January 21\u201323). Classification of tea samples using SVM as machine learning component of E-tongue. Proceedings of the 2016 International Conference on Intelligent Control Power and Instrumentation, Kolkata, India.","DOI":"10.1109\/ICICPI.2016.7859673"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.dcmed.2022.10.002","article-title":"Tongue image segmentation and tongue color classification based on deep learning","volume":"5","author":"Liu","year":"2022","journal-title":"Digit. Chin. Med."},{"key":"ref_11","first-page":"5667","article-title":"Intelligent deep learning based disease diagnosis using biomedical tongue images","volume":"70","author":"Thanikachalam","year":"2022","journal-title":"CMC\u2014Comput. Mater. Contin."},{"key":"ref_12","first-page":"4727","article-title":"A fast tongue detection and location algorithm in natural environment","volume":"73","author":"Zhu","year":"2022","journal-title":"CMC\u2014Comput. Mater. Contin."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1177\/03611981241258753","article-title":"CTAFFNet: CNN\u2013transformer adaptive feature fusion object detection algorithm for complex traffic scenarios","volume":"2679","author":"Dong","year":"2025","journal-title":"Transp. Res. Rec."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104622","DOI":"10.1016\/j.compbiomed.2021.104622","article-title":"Application of computer tongue image analysis technology in the diagnosis of NAFLD","volume":"135","author":"Jiang","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1515\/mr-2021-0018","article-title":"Digital tongue image analyses for health assessment","volume":"1","author":"Xie","year":"2021","journal-title":"Med. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, X., and Zhang, D. (2010, January 28\u201330). A Comparative study of color correction algorithms for tongue image inspection. Proceedings of the International Conference on Medical Biometrics, Hong Kong, China.","DOI":"10.1007\/978-3-642-13923-9_42"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chang, W.H., Chen, C.C., Wu, H.K., Hsu, P.C., Lo, L.C., Chu, H.T., and Chang, H.H. (2024). Tongue feature dataset construction and real-time detection. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0296070"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ding, J., Cao, G., and Meng, D. (2016, January 29\u201330). Classification of tongue images based on doublet SVM. Proceedings of the 2016 International Symposium on System and Software Reliability, Shanghai, China.","DOI":"10.1109\/ISSSR.2016.021"},{"key":"ref_19","first-page":"897580","article-title":"Significant geometry features in tongue image analysis","volume":"1","author":"Zhang","year":"2015","journal-title":"Evid.-Based Complement. Altern. Med."},{"key":"ref_20","first-page":"912852","article-title":"Automated tongue feature extraction for ZHENG classification in traditional Chinese medicine","volume":"1","author":"Kanawong","year":"2012","journal-title":"Evid.-Based Complement. Altern. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hou, J., Su, H.Y., Yan, B., Zheng, H., Sun, Z.L., and Cai, X.C. (2017, January 10\u201312). Classification of tongue color based on CNN. Proceedings of the 2017 IEEE 2nd International Conference on Big Data Analysis, Beijing, China.","DOI":"10.1109\/ICBDA.2017.8078731"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, H., Hu, G., and Zhang, X. (2018, January 13\u201315). Constitution identification of tongue image based on CNN. Proceedings of the 2018 11th International Congress on Image and Signal Processing, Beijing, China.","DOI":"10.1109\/CISP-BMEI.2018.8633075"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, X., Wang, S., Lv, H., Yu, Y., and Wang, Y. (2025, January 23\u201325). Lightweight CNN-SVM based hybrid model for tongue image classification. Proceedings of the 2025 5th Asia-Pacific Conference on Communications Technology and Computer Science, Shenyang, China.","DOI":"10.1109\/ACCTCS66275.2025.00087"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, C., Zhang, D., and Chen, S. (2020, January 12\u201314). Research about tongue image of traditional Chinese medicine (TCM) based on artificial intelligence technology. Proceedings of the 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference, Chongqing, China.","DOI":"10.1109\/ITOEC49072.2020.9141557"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, B., Xu, X., and Ye, H. (2024, January 14\u201316). Diagnosis system using tongue coating color based on improved YOLOv5s. Proceedings of the 2024 IEEE 6th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, Taiwan, China.","DOI":"10.1109\/ECBIOS61468.2024.10885486"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montr\u00e9al, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_28","unstructured":"Lei, M., Li, S., Wu, Y., Hu, H., Zhou, Y., Zheng, X., Ding, G., Du, S., Wu, Z., and Gao, Y. (2025). YOLOv13: Real-time object detection with hypergraph-enhanced adaptive visual perception. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/219\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:35:40Z","timestamp":1767191740000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26010219"],"URL":"https:\/\/doi.org\/10.3390\/s26010219","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]}}}